Increasing the resolution of images is useful in providing viewers with a better observation experience. To this end, image interpolation is used in many real world applications to fill in missing pixels generally based on surrounding information. In general, two criteria are used to evaluate the performance of an image interpolator, namely perceptual quality and computational complexity.
Conventional linear operators like bilinear and bicubic image interpolation are relatively simple and fast, but often introduce annoying “baggy” artifacts around the edges, primarily because local features in images are not taken into consideration. Therefore, various adaptive image interpolators have been implemented in an attempt to better preserve the edges, by utilizing more accurate models.
However, such models suffer from a number of drawbacks, including computational inefficiency. For example, due to the iterative property and/or significant complexity of reliable estimation of adaptive coefficients, the overall computational cost may be much higher than that of linear interpolators, even when hybrid algorithms are used to reduce the complexity.
Another drawback is that some models limit edge orientations to several predefined choices, which affects the accuracy of the imposed model. Other interpolators have a limited interpolation ratio, that is, many interpolators are restricted to a ratio of 2n; interpolation to another ratio requires re-sampling from a higher 2n image.